Effect of Euler number as a feature in gender recognition system from offline handwritten signature using neural networks

Recent growth of technology has also increased identification insecurity. Signature is a unique feature which is different for every other person, and each person can be identified using their own handwritten signature. Gender identification is one of key feature in case of human identification. In this paper, a feature based gender detection method has been proposed. The proposed framework takes handwritten signature as an input. Afterwards, several features are extracted from those images. The extracted features and their values are stored as data, which is further classified using Back Propagation Neural Network (BPNN). Gender classification is done using BPNN which is one of the most popular classifier. The proposed system is broken into two parts. In the first part, several features such as roundness, skewness, kurtosis, mean, standard deviation, area, Euler number, distribution density of black pixel, entropy, equi-diameter, connected component (cc) and perimeter were taken as feature. Then obtained features are divided into two categories. In the first category experimental feature set contains Euler number, whereas in the second category the obtained feature set excludes the same. BPNN is used to classify both types of feature sets to recognize the gender. Our study reports an improvement of 4.7% in gender classification system by the inclusion of Euler number as a feature.